Cancer and breast density: What are doctors withholding?

The Japan Times

October is Pink Ribbon Month: an annual campaign to increase awareness about breast cancer and get more women screened in order to catch the disease in its early stages, which will boost survival rates. And as part of the campaign, buildings are lit up in pink and cancer survivors and doctors hold lectures nationwide to get the message out. But experts in Japan remain divided -- and undecided -- on one issue surrounding breast cancer screenings and that is whether to tell people who undergo the tests if they have dense breast tissue. Normal breast tissue is composed of milk glands, milk ducts, fatty tissue and supportive tissue that is dense breast tissue. For those with dense breasts, they have more dense tissue than fatty tissue.


A tree augmented naive Bayesian network experiment for breast cancer prediction

arXiv.org Machine Learning

In order to investigate the breast cancer prediction problem on the aging population with the grades of DCIS, we conduct a tree augmented naive Bayesian network experiment trained and tested on a large clinical dataset including consecutive diagnostic mammography examinations, consequent biopsy outcomes and related cancer registry records in the population of women across all ages. The aggregated results of our ten-fold cross validation method recommend a biopsy threshold higher than 2% for the aging population.


Combination of AI and radiologists more accurately identified breast cancer

#artificialintelligence

An artificial intelligence (AI) tool--trained on roughly a million screening mammography images--identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists, a new study finds. Led by researchers from NYU School of Medicine and the NYU Center for Data Science, the study examined the ability of a type of AI, a machine learning computer program, to add value to the diagnoses reached by a group of 14 radiologists as they reviewed 720 mammogram images. "Our study found that AI identified cancer-related patterns in the data that radiologists could not, and vice versa," says senior study author Krzysztof J. Geras, Ph.D., assistant professor in the Department of Radiology at NYU Langone. "AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI," adds Dr. Geras, also an affiliated faculty member at the NYU Center for Data Science. "The ultimate goal of our work is to augment, not replace, human radiologists."


Combination of Artificial Intelligence and Radiologists More Accurately Identified Breast Cancer

#artificialintelligence

An artificial intelligence (AI) tool--trained on roughly a million screening mammography images--identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists, a new study finds. Led by researchers from NYU School of Medicine and the NYU Center for Data Science, the study examined the ability of a type of AI, a machine learning computer program, to add value to the diagnoses reached by a group of 14 radiologists as they reviewed 720 mammogram images. "Our study found that AI identified cancer-related patterns in the data that radiologists could not, and vice versa," says senior study author Krzysztof J. Geras, PhD, assistant professor in the Department of Radiology at NYU Langone. "AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI," adds Dr. Geras, also an affiliated faculty member at the NYU Center for Data Science. "The ultimate goal of our work is to augment, not replace, human radiologists."


Combination of Artificial Intelligence and Radiologists More Accurately Identified Breast Cancer

#artificialintelligence

An artificial intelligence (AI) tool--trained on roughly a million screening mammography images--identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists, a new study finds. Led by researchers from NYU School of Medicine and the NYU Center for Data Science, the study examined the ability of a type of AI, a machine learning computer program, to add value to the diagnoses reached by a group of 14 radiologists as they reviewed 720 mammogram images. "Our study found that AI identified cancer-related patterns in the data that radiologists could not, and vice versa," says senior study author Krzysztof J. Geras, PhD, assistant professor in the Department of Radiology at NYU Langone. "AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI," adds Dr. Geras, also an affiliated faculty member at the NYU Center for Data Science. "The ultimate goal of our work is to augment, not replace, human radiologists."